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Transcranial Magnetic Stimulation in Migraine: A New Therapy and New Insights into Pathogenesis

2016· article· en· W2614945759 on OpenAlexaff
Alan J. McComas, A.R.M. Upton

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2016
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMigraineTranscranial magnetic stimulationAuraCortical spreading depressionMigraine with auraMedicineNeurosciencePathogenesisDepression (economics)Adverse effectStimulationPhysical medicine and rehabilitationPsychologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Transcranial magnetic stimulation (TMS) was first found to be effective in acute migraine 12 years ago, and subsequent studies have confirmed this benefit in approximately two -thirds of treated patients. High response rate, ease of application, and freedom from adverse effects combine to make TMS a natural front-line treatment for migraine, and its use should therefore be encouraged. In relation to the pathogenesis of migraine, the prompt relief of symptoms often observed with TMS is considered incompatible with an underlying neuroinflammatory process and with spreading depression as a cause of aura. Instead, the available evidence points to hyperexcitability of cortical neurons as the immediate cause of headache and any associated symptoms, although the factor(s) leading up to the hyperexcitability remain unclear.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.317
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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